1. Field of the Invention
The invention relates to the process of inspecting integrated circuit images. More specifically, the invention relates to a method and an apparatus to facilitate auto-alignment of mask and die images of integrated circuits for defect inspection and/or defect analysis.
2. Related Art
Integrated circuits can be produced through an optical lithography process that involves creating a mask with a pattern specifying where the various features of the integrated circuit are to be placed and then passing radiation through the mask to expose the pattern on a semiconductor wafer. This pattern defines where the surface of the semiconductor wafer is to be etched or where new material is to be added to create the integrated circuit.
As the features of an integrated circuit continue to get smaller, quality control becomes increasingly important in order to ensure that the integrated circuit functions properly. As part of this quality control, integrated circuit manufacturers often compare various images of an integrated circuit; for example, a manufacturer may compare a computer-generated image of the integrated circuit to a mask of the integrated circuit or may compare the mask to a die created from the mask. These comparisons can determine if defects exist and can help determine the cause of these defects.
These comparisons can be made by first aligning the images being compared and then subtracting, pixel-by-pixel, the reference image from the test image. The resultant difference is ideally zero for all pixels. Differences other than zero may indicate a defect in the test image, which can be analyzed to determine the severity of the defect, and can help determine the cause of the defect. During this defect analysis process, accurate alignment of the images is critical for this process to yield the expected results.
Current systems use an auto-correlation method to align these images. Auto-correlation is a very slow process because it requires a computationally intensive mathematical process to be performed pixel-by-pixel on the images. Also, the success rate of auto-correlation is not very high. The auto-correlation algorithm attempts to maximize the correlation coefficient:   c  =            ∑                        (                                    x                              i                ,                j                                      -                          x              _                                )                ×                  ∑                      (                                          y                                  i                  ,                  j                                            -                              y                _                                      )                                                        (                      ∑                                          (                                                      x                                          i                      ,                      j                                                        -                                      x                    _                                                  )                            2                                )                          1          /          2                    ×                        (                      ∑                                          (                                                      y                                          i                      ,                      j                                                        -                                      y                    _                                                  )                            2                                )                          1          /          2                    
where xi,j and yi,j are the pixel values of the images at the respective location i and j, and {overscore (x)} and {overscore (y)} are the mean values of each image. Thus the auto-correlation algorithm is searching for a location by shifting the two images around to maximize the coefficient. This is an intensive calculation and the range of the shifted positions that are tried will limit the quality of the found position.
What is needed is a method and an apparatus to facilitate auto-alignment of integrated circuit images for defect inspection and defect analysis that do not exhibit the problems described above.